Abstract

Studies shows that finding frequent sub-graphs in uncertain graphs database is an NP complete problem. Finding the frequency at which these sub-graphs occur in uncertain graph database is also computationally expensive. This paper focus on investigation of mining frequent sub-graph patterns in DBLP uncertain graph data using an approximation based method. The frequent sub-graph pattern mining problem is formalized by using the expected support measure. Here n approximate mining algorithm based Weighted MUSE, is proposed to discover possible frequent sub-graph patterns from uncertain graph data

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